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JP-7856388-B2 - Performance prediction device and program

JP7856388B2JP 7856388 B2JP7856388 B2JP 7856388B2JP-7856388-B2

Inventors

  • 古渡 直哉

Assignees

  • 横浜ゴム株式会社

Dates

Publication Date
20260511
Application Date
20210407

Claims (4)

  1. A machine learning model that is trained using multiple training manufacturing condition vector data, each of which vectorizes multiple training manufacturing condition data representing the manufacturing conditions of a product, each of which includes multiple factors representing the manufacturing conditions of the product, and multiple performance data representing the properties of the product manufactured by the manufacturing conditions indicated by the multiple factors included in each of the multiple training manufacturing condition vector data, and which outputs predictive performance data representing the properties of the product manufactured by the manufacturing conditions when input manufacturing condition vector data including multiple factors representing the manufacturing conditions is input, A dimensionality reduction means that inputs each of the plurality of training manufacturing condition vector data into the machine learning model and obtains the output from the intermediate layer of the machine learning model to reduce the dimensionality of each of the plurality of training manufacturing condition vector data , and inputs the input manufacturing condition vector data into the machine learning model and obtains the output from the intermediate layer of the machine learning model to reduce the dimensionality of the input manufacturing condition vector data , Similarity determination means for determining the similarity between the dimensionality-reduced input manufacturing condition vector data and each of the dimensionality-reduced plurality of training manufacturing condition vector data, Output means for outputting the aforementioned prediction performance data, and outputting information indicating the degree of similarity between the dimensionality-reduced input manufacturing condition vector data and any of the dimensionality-reduced plurality of training manufacturing condition vector data as the accuracy of the prediction performance data, A performance prediction device characterized by including [a certain feature].
  2. In the performance prediction device according to claim 1 , The aforementioned product is a tire, The plurality of learning manufacturing condition vector data and the input manufacturing condition vector data each include one or more factors representing the material of the tire and one or more factors representing the structure of the tire. The aforementioned predicted performance data indicates the performance of the tire. A performance prediction device characterized by the following features.
  3. In the performance prediction device according to claim 1 or 2 , The aforementioned product is tire rubber, The plurality of learning manufacturing condition vector data and the input manufacturing condition vector data each include one or more factors representing the rubber material and one or more factors representing the rubber manufacturing conditions. The aforementioned predictive performance data indicates the physical properties of the rubber. A performance prediction device characterized by the following features.
  4. A machine learning model that is trained using multiple training manufacturing condition vector data, each of which is a vector of multiple training manufacturing condition data representing the manufacturing conditions of a product, each of which includes multiple factors representing the manufacturing conditions of the product , and multiple performance data representing the properties of the product manufactured by the manufacturing conditions indicated by the multiple factors included in each of the multiple training manufacturing condition vector data , and input manufacturing condition vector data, which is a vector of input manufacturing condition data representing the manufacturing conditions of the product, and outputs predictive performance data representing the properties of the product manufactured by the manufacturing conditions when input manufacturing condition vector data including multiple factors representing the manufacturing conditions is input. Dimensionality reduction means that inputs each of the plurality of training manufacturing condition vector data into the machine learning model and obtains the output from the intermediate layer of the machine learning model to reduce the dimensionality of each of the plurality of training manufacturing condition vector data , inputs the input manufacturing condition vector data into the machine learning model and obtains the output from the intermediate layer of the machine learning model to reduce the dimensionality of the input manufacturing condition vector data , Similarity determination means for determining the similarity between the dimensionally compressed input manufacturing condition vector data and each of the dimensionally compressed plurality of training manufacturing condition vector data; and output means for outputting the prediction performance data and outputting information indicating the degree of similarity between the dimensionally compressed input manufacturing condition vector data and any of the dimensionally compressed plurality of training manufacturing condition vector data as the accuracy of the prediction performance data. A program that makes a computer function.

Description

This disclosure relates to a performance prediction device and program. It is known that machine learning models obtained using machine learning techniques such as neural networks are used to make predictions on given input manufacturing condition data. For example, Patent Document 1 describes how, when an unpredicted prediction case is input, a set of similar cases is extracted from a set of existing prediction cases, the confidence level of a certain prediction attribute value is calculated from the set of similar cases, a reliability scale is calculated from the set of similar cases and the confidence level, and the confidence level of a certain prediction attribute value and the reliability scale of that confidence level are output. Furthermore, Patent Document 2 describes how an input dataset is divided according to specified division conditions, neighbor data of the input data including feature nodes that represent the characteristics of the distribution structure of each divided dataset is generated, and a score representing the relationship between the explanatory variables and the target variable is calculated based on the explanatory variables of the generated neighbor data and the target variable data obtained by inputting the neighbor data into a machine learning model. Japanese Patent Publication No. 2003-323601Japanese Patent Publication No. 2019-191895 This figure shows the configuration of a performance prediction device, which is an example of an embodiment of the present disclosure.This is a functional block diagram showing an example of the functions implemented in the performance prediction device.This figure shows an example of training data and training-time dimensionality reduction data.This figure shows an example of input data and input-time dimensionality-compressed data.This figure shows an example of prediction performance data and the accuracy of that data.This figure shows an example of the processes performed when training a machine learning model.This figure shows an example of the processes performed when training a machine learning model.This figure shows an example of the processing performed when making predictions on input data.This figure shows an example of the processing performed when making predictions on input data.This is a functional block diagram showing another example of the functions implemented in the performance prediction device. The embodiments of this disclosure will be described below with reference to the drawings. In this embodiment, input data indicating the manufacturing conditions of a product is input to a trained machine learning model, which outputs predictive performance data indicating the properties of the product manufactured under those manufacturing conditions, as well as information regarding the accuracy of that predictive performance data. The following description will focus on the case where the product is a tire or tire rubber, but the product is not limited to mixtures such as tires or tire rubber produced by multiple raw materials and manufacturing processes; it can be any substance produced under predetermined conditions. [1. Hardware Configuration] Figure 1 shows the configuration of a performance prediction device 10, which is an example of an embodiment of the present disclosure. The performance prediction device 10 according to this embodiment is a computer such as a personal computer, a general-purpose computer, or a portable information terminal, and as shown in Figure 1, it includes a processor 11, a storage unit 12, a communication unit 13, a display unit 14, and an operation unit 15. The performance prediction device 10 may also include an optical disc drive for reading optical discs, a USB (Universal Serial Bus) port, and the like. The processor 11 is a program-controlled device, such as a CPU (Central Processing Unit), that operates according to a program installed in the performance prediction device 10, which is a computer. The storage unit 12 consists of memory elements such as ROM (Read Only Memory) and RAM (Random Access Memory), or a hard disk drive. The storage unit 12 stores data such as programs executed by the processor 11. The communication unit 13 is a communication interface, such as a network board. The display unit 14 is a display device, such as a liquid crystal display, that displays various images according to instructions from the processor 11. The operation unit 15 is a user interface, such as a keyboard or mouse, that receives user input and outputs signals indicating the content of that input to the processor 11. [2. Functional Blocks] The performance prediction device 10 outputs predicted performance data indicating the predicted performance of a tire or the rubber of the tire (for example, the rolling resistance of the tire or the hardness of the rubber), and also outputs information regarding the accuracy of the predicted performance data. The output of predicted performance data and information